To main content

Hybrid intelligent framework for automated medical learning


This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutions.
Read publication


Academic article




  • Asma Belhadi
  • Youcef Djenouri
  • Vicente Garcia Diaz
  • Essam H. Houssein
  • Jerry Chun-Wei Lin


  • Kristiania University College
  • SINTEF Digital / Mathematics and Cybernetics
  • University of Oviedo
  • Minia University
  • Western Norway University of Applied Sciences



Published in

Expert Systems




John Wiley & Sons


1 - 14

View this publication at Cristin